Statistical image processing system and method for image/noise feature detection

ABSTRACT

A statistical image processing system and method for detecting an original image feature and/or a noise feature in an input image using a statistical hypothesis test, thereby estimating the image or noise when an original image is contaminated with the noise, and a recording medium for recording the method, are provided. The method enables existence/non-existence of an image/noise feature in an input image to be accurately detected without information on the magnitude of a sample variance of noise in the input image. In addition, according to the method, the relative amount of noise and/or image feature can be numerically expressed and can be used in the various fields of image processing application.

CROSS-REFERENCE TO RELATED PATENT APPLICATION

This application claims the benefit of Korean Patent Application No. 10-2005-0115811, filed on Nov. 30, 2005, in the Korean Intellectual Property Office, the disclosure of which is incorporated herein in its entirety by reference.

FIELD OF THE INVENTION

The present invention relates to a statistical image processing system and method for image/noise feature detection, and more particularly, to a statistical image processing system and method for detecting an original image feature and/or a noise feature in an input image using a statistical hypothesis test, thereby estimating the image or noise when an original image is contaminated with the noise, and a recording medium for recording the method.

BACKGROUND OF THE INVENTION

Conventionally, a method using a sample variance usually defined in statistics is used in order to detect an original image feature and a noise feature in an input image. In this conventional method, image sample data that has a small sample variance is detected as a noise feature and image sample data that has a large sample variance is detected as an image feature.

Referring to FIG. 1, sample (a) is an enlarged view of a part of an image having a small detail of an original image and having a slight of Gaussian noise while sample (b). is an enlarged view of a part of an image having only Gaussian noise without an image feature. Both of the samples (a) and (b) have similar sample variances of about 25.

Referring to FIG. 2, sample (a) corresponds to an edge while sample (b) shows an image having only Gaussian noise without an image feature. However, both of the samples (a) and (b) have similar sample variances of about 2500.

It can be inferred from samples (a) and (b) illustrated in FIGS. 1 and 2 that there is a limit in detecting an image feature or a noise feature in an input image based on only the value of a sample variance. Moreover, since most image processing application systems do not possess advance information on the variance of noise complying with the Gaussian distribution N(0, σ²), it is difficult to provide coherent results regardless of the value of the variance of noise.

SUMMARY OF THE INVENTION

Some embodiments of the present invention provide a statistical image processing system and method for detecting an image feature and a noise feature in an input image using a statistical hypothesis test and numerically expressing the image feature or the noise feature in the input image using statistical inference to estimate the image feature or a noise feature in each pixel when the input image is contaminated with noise, and a recording medium for recording a program for executing the method on a computer.

Some embodiments of the present invention provide a statistical image processing system and method for applying statistics to existing image processing applications with limit in performance due to inaccurate detection of an image feature and a noise feature, thereby enhancing adaptiveness to noise, and a recording medium for recording a program for executing the method on a computer.

Other embodiments of the present invention provide a method for estimating an image feature and a noise feature in each pixel in an input image, by which the amount of image/noise feature is independently detected using the statistical properties of image data in a portion or a block including a pixel to be estimated.

According to some embodiments of the present invention, there is provided a statistical image processing system for detecting an image/noise feature. The system includes a partial image extraction unit configured to extract a partial image surrounding a particular pixel in an input image, an image correlation estimation unit configured to obtain a direction coherence with respect to each of a plurality of predetermined direction using a correlation between pixel values in the partial image, an independence estimation unit configured to obtain a test statistic by numerically expressing similarity between a plurality of direction coherences obtained by the image correlation estimation unit, and a statistical hypothesis test unit configured to compare the test statistic obtained by the independence estimation unit with at least one predetermined image/noise limit and to obtain an image/noise detection value indicating an amount of image/noise feature in the partial image.

According to other embodiments of the present invention, there is provided a statistical image processing method for detecting an image/noise feature. The method includes extracting a partial image surrounding a particular pixel in an input image, obtaining a direction coherence with respect to each of a plurality of predetermined direction using a correlation between pixel values in the partial image, obtaining a test statistic by numerically expressing similarity between a plurality of direction coherences, and obtaining an image/noise detection value indicating an amount of image/noise feature in the partial image by comparing the test statistic with at least one predetermined image/noise limit.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings in which:

FIG. 1 illustrates two samples respectively corresponding to an image feature and a noise feature and having the same variance of 25;

FIG. 2 illustrates two samples respectively corresponding to an image feature and a noise feature and having the same variance of 2500;

FIG. 3 is a block diagram of a statistical image processing system for detecting an image/noise feature according to some embodiments of the present invention;

FIG. 4 is a flowchart of a statistical image processing method for detecting an image/noise feature according to some embodiments of the present invention;

FIG. 5 illustrates the relationship among an entire image, a pixel in which an image/noise feature is detected, and a block image surrounding the pixel in operation S100 in the method illustrated in FIG. 4;

FIG. 6 illustrates four directions in which pixels in an M×N block are rearranged in order to detect a direction coherence of an image in operation S200 in the method illustrated in FIG. 4;

FIG. 7 illustrates a scheme of detecting the direction coherence of an image using a pixel difference between two M×N blocks in operation S200 in the method illustrated in FIG. 4;

FIG. 8 is a detailed flowchart of operation S400 in the method illustrated in FIG. 4;

FIG. 9 is a graph of an image/noise detection function according to some embodiments of the present invention;

FIG. 10 is a graph of an image/noise detection function according to some embodiments of the present invention;

FIG. 11 is a graph of an image/noise detection function according to some embodiments of the present invention;

FIG. 12 is a detailed flowchart of a procedure for obtaining two image/noise limits using a direction coherence in the method illustrated in FIG. 4; and

FIG. 13 is a block diagram of a real application using a statistical image processing system and method for detecting an image/noise feature according to some embodiments of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

The present invention now will be described more fully hereinafter with reference to the accompanying drawings, in which embodiments of the invention are shown. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. In the drawings, like numbers refer to like elements throughout.

It will be understood that when an element is referred to as being “connected” or “coupled” to another element, it can be directly connected or coupled to the other element or intervening, elements may be present. In contrast, when an element is referred to as being “directly connected” or “directly coupled” to another element, there are no intervening elements present. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items and may be abbreviated as “/”.

It will be understood that, although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first signal could be termed a second signal, and, similarly, a second signal could be termed a first signal without departing from the teachings of the disclosure.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” or “includes” and/or “including” when used in this specification, specify the presence of stated features, regions, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, regions, integers, steps, operations, elements, components, and/or groups thereof.

Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and/or the present application, and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

FIG. 3 is a block diagram of a statistical image processing system for detecting an image/noise feature according to some embodiments of the present invention. Referring to FIG. 3, the image processing system includes a partial image extraction unit 10, an image correlation estimation unit 20, an independence estimation unit 30, and a statistical hypothesis test unit 40.

The partial image extraction unit 10 extracts pixel data of a partial image, and usually, a block image M_(N×N)(l,m) surrounding a particular pixel, in which an image/noise feature is detected, from an input image, e.g., entire image data.

The image correlation estimation unit 20 defines a direction coherence V_(θ)(l,m) indicating the degree of coherence between pixel values with respect to a direction θ within the partial image M_(N×N)(l,m) set by the partial image extraction unit 10 and obtains the direction coherence V_(θ)(l,m) (where θ=θ₁, θ₂, . . . , θ_(p) and “p” is 2 or an integer greater than 2) with respect to each of a plurality of predetermined directions.

The independence estimation unit 30 obtains a test statistic Z which numerically expresses the similarity between the plurality of direction coherences V_(θ)(l,m) obtained by the image correlation estimation unit 20.

The statistical hypothesis test unit 40 compares the test statistic Z obtained by the independence estimation unit 30 with one or more predetermined image/noise limits β₀ and β₁ to obtain an image/noise detection value H(l,m) indicating the amount of image/noise feature in the partial image M_(N×N)(l,m).

FIG. 4 is a flowchart of a statistical image processing method for detecting an image/noise feature according to some embodiments of the present invention. In operation S100, the partial image extraction unit 10 extracts a pixel value of a partial image, and usually, a block image M_(N×N)(l,m) surrounding a particular pixel, in which an image/noise feature is detected, from an entire image. At this time, the shape of the partial image may be arbitrarily defined. In FIG. 5, for clarity of the description, the shape of the partial image is defined as an N×N square.

FIG. 5 illustrates the relationship among the entire image, the particular pixel in which an image/noise feature is detected, and the block image M_(N×N)(l,m) surrounding the particular pixel in operation S100 in the method illustrated in FIG. 4. Referring to FIG. 5, the partial block image M_(N×N)(l,m) has the shape of a 5×5 square having 5 pixels in length and 5 pixels in width. However, the size of the partial block m ay vary with applications. The particular pixel within the entire image may be represented with coordinates in the entire image. For example, when an upper left corner in the entire image is represented with (1,1), a pixel positioned on an l-th row from the top of the entire image and an m-th column from the left of the entire image is represented with f(l,m). The partial image having the N×N square shape defined on the basis of the particular pixel is represented with M_(N×N)(l,m). In FIG. 5, the partial image is defined as a 5×5 square on the basis of the particular pixel positioned on the seventh row and the sixth column in the entire image and is thus represented with M_(5×5)(7,6).

Referring back to FIG. 4, in operation S200, the image correlation estimation unit 20 obtains a direction coherence V_(θ)(l,m) with respect to each of the plurality of predetermined directions θ (where θ=θ₁, θ₂, . . . , θ_(p) and “p” is 2 or an integer greater than 2) using correlations between pixel values within the partial image M_(N×N)(l,m). At this time, the direction coherence V_(θ)(l,m) may be redefined by multiplying the direction coherence V_(θ)(l,m) by an appropriate constant. Schemes of obtaining the change in a pixel value in a particular direction within the partial image M_(N×N)(l,m) in order to obtain the direction coherence V_(θ)(l,m) are illustrated in FIGS. 6 and 7.

FIG. 6 illustrates how pixels within the partial image M_(N×N)(l,m) are rearranged in a given direction in order to define the direction coherence V_(θ)(l,m) in operation S200 in the method illustrated in FIG. 4. Here, pixels values in the partial image M_(N×N)(l,m) are arranged on a line along a predetermined direction. Thereafter, the direction coherence V_(θ)(l,m) may be defined based on a function having as a factor a difference Z_(i) ^(θ) between adjacent pixel values, which is expressed by z_(i) ^(θ)=u_(i) ^(θ)−u_(i−1) ^(θ) or z_(i) ^(θ)=|u_(i) ^(θ)−u_(i−1) ^(θ)|. For example, the direction coherence V_(θ)(l,m) may be defined as an average of the function having the difference Z_(i) ^(θ) as a factor. Here, “θ” indicates an angle of a direction in which all pixel values in the partial image M_(N×N)(l,m) are read and “u_(i) ^(θ)” indicates an i-th pixel value when the pixel values in the partial image M_(N×N)(!,m) are sequentially read at the direction angle θ.

Referring to FIG. 6, the pixel values in the partial image M_(N×N)(l,m) may be sequentially read in four directions respectively having angles of 0°, 90°, 45°, and 135° and arranged in a one-dimensional array. Thereafter, differences between adjacent pixel values may be statistically and numerically expressed to obtain a direction coherence V_(θ). For example, the direction coherence V_(θ) may be defined by Equation (1) or (2).

$\begin{matrix} {V_{\theta} = {\frac{1}{N^{2} - 1}{\sum\limits_{i = 2}^{N^{2}}\left( z_{i}^{\theta} \right)^{2}}}} & (1) \\ {V_{\theta} = {\frac{1}{N^{2} - 1}{\sum\limits_{i = 2}^{N^{2}}{z_{i}^{\theta}}}}} & (2) \end{matrix}$

Here, z_(i) ^(θ)=u_(i) ^(θ)−u_(i−1) ^(θ) and “u_(i) ^(θ)” indicates the i-th pixel value when the pixel values in the partial image M_(N×N)(l,m) are sequentially read at the direction angle θ. A total number of pairs of adjacent two pixels is N²−1 , and therefore, a total of number of values z_(i) ^(θ) is also N²−1 from i=2 to i=N².

When the direction coherence V_(θ) is defined by Equation (1), the direction coherence V_(θ) has similar values without a significant difference regardless of the direction θ if data in the partial image M_(N×N)(l,m) has a random Gaussian distribution. However, if the data in the partial image M_(N×N)(l,m) has a image feature in a particular direction, z_(i) ^(θ) has smaller values in the particular direction than in other directions and the direction coherence V_(θ) is also decreased in the particular direction while the direction coherence V_(θ) is increased in directions far from the particular direction of the image feature. Accordingly, the direction coherence V_(θ) is different according to the direction θ in the partial image M_(N×N)(l,m) including the image feature.

When all pixels in the partial image M_(N×N)(l,m) are arranged in an N×N matrix, the direction coherence V_(θ) may be variously defined by, for example, Equations (3), (4), and (5).

$\begin{matrix} {{V_{\theta} = {\frac{1}{N^{2} - 1}{\sum\limits_{i = 2}^{N^{2}}\left( {z_{i}^{\theta} - \overset{\_}{z}} \right)^{2}}}},{\overset{\_}{z} = {\frac{1}{N^{2} - 1}{\sum\limits_{i = 2}^{N^{2}}z_{i}^{\theta}}}}} & (3) \\ {{V_{\theta} = {\frac{1}{N^{2} - 1}{\sum\limits_{i = 2}^{N^{2}}\left( {\left\lbrack z_{i}^{\theta} \right\rbrack - \overset{\_}{z}} \right)^{2}}}},{\overset{\_}{\overset{\_}{z}} = {\frac{1}{N^{2} - 1}{\sum\limits_{i = 2}^{N^{2}}z_{i}^{\theta}}}}} & (4) \\ {{V_{\theta} = {\frac{1}{N^{2} - 1}{\sum\limits_{i = 2}^{N^{2}}\left( {{z_{i}^{\theta}} - \overset{\_}{z}} \right)^{2}}}},{\overset{\_}{z} = {\frac{1}{N^{2} - 1}{\sum\limits_{i = 2}^{N^{2}}z_{i}^{\theta}}}}} & (5) \end{matrix}$

FIG. 7 illustrates another scheme of defining the direction coherence V_(θ) of the partial image M_(N×N)(l,m) in operation S200 in the method illustrated in FIG. 4. Referring to FIG. 7, a second partial image M_(N×N)(l+Δ_(l),m+Δm) apart from the first partial image M_(N×N)(l,m) by a distance of (Δ_(l),Δ_(m)) is defined. Differences between pixels in the first partial image M_(N×N)(l,m) and respective corresponding pixels in the second partial image M_(N×N)(l+Δ_(l),m+Δ_(m)) are statistically and numerically expressed to obtain the direction coherence V_(θ). In detail, the direction coherence V_(θ) may be defined based on a function having as a factor a pixel difference z_(i,j) ^(θ) between the first and second partial images. For example, the direction coherence V_(θ) may be defined as an average of the function having the pixel difference z_(ij) ^(θ) as a factor. For example, the direction coherence V_(θ) may be defined by Equation (6) or (7).

$\begin{matrix} {{V_{\theta}\left( {l,m} \right)} = {\frac{1}{N^{2}}{\sum\limits_{i = 1}^{N}{\sum\limits_{j = 1}^{N}\left( z_{i,j}^{\theta} \right)^{2}}}}} & (6) \\ {{V_{\theta}\left( {l,m} \right)} = {\frac{1}{N^{2}}{\sum\limits_{i = 1}^{N}{\sum\limits_{j = 1}^{N}{{z_{i,j}^{\theta}}.}}}}} & (7) \end{matrix}$

In addition, when all pixels in the partial image M_(N×N)(l,m) are arranged in an N×N matrix, the direction coherence V_(θ) may be variously defined by, for example, Equations (8), (9), and (10).

$\begin{matrix} {{{V_{\theta}\left( {l,m} \right)} = {\frac{1}{N^{2}}{\sum\limits_{i = 1}^{N}{\sum\limits_{j = 1}^{N}\left( {z_{i,j}^{\theta} - \overset{\_}{z}} \right)^{2}}}}},{\overset{\_}{z} = {\frac{1}{N^{2}}{\sum\limits_{i = 1}^{N}{\sum\limits_{j = 1}^{N}z_{i,j}^{\theta}}}}}} & (8) \\ {{{V_{\theta}\left( {l,m} \right)} = {\frac{1}{N^{2}}{\sum\limits_{i = 1}^{N}{\sum\limits_{j = 1}^{N}\left( {\left\lbrack z_{i,j}^{\theta} \right\rbrack^{2} - \overset{\_}{z}} \right)^{2}}}}},{\overset{\_}{z} = {\frac{1}{N^{2}}{\sum\limits_{i = 1}^{N}{\sum\limits_{j = 1}^{N}z_{i,j}^{\theta}}}}}} & (9) \\ {{{V_{\theta}\left( {l,m} \right)} = {\frac{1}{N^{2}}{\sum\limits_{i = 1}^{N}{\sum\limits_{j = 1}^{N}\left( {{z_{i,j}^{\theta}} - \overset{\_}{z}} \right)^{2}}}}},{\overset{\_}{z} = {\frac{1}{N^{2}}{\sum\limits_{i = 1}^{N}{\sum\limits_{j = 1}^{N}z_{i,j}^{\theta}}}}}} & (10) \end{matrix}$

Here, “i” and “j” respectively indicate a row and a column in each partial image, θ indicates a direction in which the second partial image M_(N×N)(l+Δ_(l),m+Δ_(m)) is apart from the first partial image M_(N×N)(l,m), and z_(i,j) ^(θ) indicates a difference between a pixel value on a coordinate position (i,j) in the first partial image M_(N×N)(l,m) and a pixel value on the coordinate position (i,j) in the second partial image M_(N×N)(l+Δ_(l),m+Δ_(m)). The difference z_(i,j) ^(θ) may be defined by Equations (11) and (12).

$\begin{matrix} {{z_{i,j}^{\theta} = {{x_{i,j}\left( {l,m} \right)} - {x_{i,j}\left( {{l + \Delta_{l}},{m + \Delta_{m}}} \right)}}},{\theta = {\tan^{- 1}\left( \frac{\Delta_{l}}{\Delta_{m}} \right)}}} & (11) \\ {{z_{i,j}^{\theta} = {{{x_{i,j}\left( {l,m} \right)} - {x_{i,j}\left( {{l + \Delta_{l}},{m + \Delta_{m}}} \right)}}}},{\theta = {\tan^{- 1}\left( \frac{\Delta_{l}}{\Delta_{m}} \right)}}} & (12) \end{matrix}$

Like the direction. coherence V_(θ) defined by Equation (1), the direction coherence V_(θ) defined by Equation (6) has similar values without a significant difference regardless of the direction θ if the data in the partial image M_(N×N)(l,m) has the random Gaussian distribution. However, if the data in the partial image M_(N×N)(l,m) has a image feature in a particular direction, z_(i,j) ^(θ) has smaller values in the particular direction than in other directions and the direction coherence V_(θ) is also decreased in the particular direction while the direction coherence V_(θ) is increased in directions far from the particular direction of the image feature. Accordingly, the direction coherence V_(θ) is different according to the direction θ in the partial image M_(N×N)(l,m) including the image feature.

The direction coherence V_(θ) defined by Equation (6) is easier and more efficient to use for image processing than the direction coherence V_(θ) defined by Equation (1). In other words, direction coherences V_(θ) with respect to various directions can be more easily obtained according to Equation (6).

In operation S300, the independence estimation unit 30 obtains the test statistic Z which numerically expresses the similarity between the plurality of direction coherences V_(θ) obtained in operation S200.

Each of the direction coherences V_(θ) obtained in operation S200 is an estimator used to estimate a population parameter from a random sample. The estimator is a single random variable and has unique distribution. The direction coherence V_(θ) defined in operation S200 is an unbiased estimator of a variance σ_(θ) ² of population with respect to the direction θ. The unbiasedness of such point estimator is a desirable characteristic that should be possessed by the estimator in point estimation in statistical inference. However, since a statistic is based on a difference between two samples adjacent in a predetermined direction, it has different values. according to the sequence of arrangement of samples, i.e., orientation θ when pixels in the partial image M_(N×N)(l,m) have an image feature. Contrarily, when pixels in the partial image M_(N×N)(l,m) are random noise, statistics V_(θ) may have values which do not have a significant difference statistically therebetween regardless of the orientation θ (where θ=θ₁, θ₂, . . . , θ_(P) and “p” is 2 or an integer greater than 2). In detail, when there is no image feature, statistics such as the direction coherence V_(θ) (where θ=θ₁, θ₂, . . . , θ_(P)) have the same statistical characteristics. In other words, when there is no image feature, the statistics V_(θ) (where θ=θ₁, θ₂, . . . , θ_(P)) are estimators related with a population variance with respect to data independently extracted from different populations. Accordingly, testing the existence of an image feature usually comes to statistical hypothesis testing for testing the sameness of a population variance among a plurality of independent regular populations. In other words, when values V₁, V₂, . . . , V_(P) obtained with respect to the orientations θ (where θ=θ₁, θ₂, . . . , θ_(P)) are determined to have a statistically significant difference, it may be concluded that an image feature exists in a given image sample. However, when the values V₁, V₂, . . . , V_(P) obtained with respect to the orientations θ (where θ=θ₁, θ₂, . . . , θ_(P)) are not determined to have a statistically significant difference, it may be concluded that the given image sample is noise. After all, it becomes the matter of testing the sameness of population variances to statistically find out whether variances of the populations are the same (in a case of random noise) or not (when an image feature exists) using the estimators V₁, V₂, . . . , V_(P) of P population variances.

To test the sameness of population variances, it is assumed that variances of regular populations with respect to P directions are respectively represented with σ₁ ², σ₂ ², . . . , σ_(P) ². Here, estimators of the variances are V₁, V₂, . . . , V_(P). A null hypothesis H₀ and an alternative hypothesis H₁ for testing the sameness of population variances are defined by Equation (13). H₀:σ₁ ²=σ₂ ²= . . . =σ_(P) ² v.s. H₁: not H₀  (13)

The null hypothesis H₀ defined by Equation (13) hypothesizes that the variances of the P populations are the same. Accordingly, when the null hypothesis H₀ is selected, the same statistical characteristics are shown in P directions. Here, it is determined that an image feature in a particular direction does not exist in a given area. However, when the null hypothesis H₀ is rejected, an image feature exists in the particular direction in the given area. However, the result of statistical hypothesis test is different with respect to a significance level, and therefore, it may be very important to determine an appropriate significance level. Accordingly, a statistical testing method of detecting whether a given area does not have an orientation, that is, the given area is a random noise area having the same statistical characteristic in all directions or whether the given area has different statistical characteristics in different directions, that is, the given area includes an image feature can be provided based on the hypotheses defined by Equation (13).

Assuming that the null hypothesis H₀: σ₁ ²=σ₂ ²= . . . =σ_(P) ² is correct, various test statistics may be used. For example, a test statistic defied by Equation (14) may be used.

$\begin{matrix} {Z = \frac{\max\begin{Bmatrix} {V_{1},} & {V_{2},\cdots\;,} & V_{P} \end{Bmatrix}}{\min\begin{Bmatrix} {V_{1},} & {V_{2},\cdots\;,} & V_{P} \end{Bmatrix}}} & (14) \end{matrix}$

Here, V₁, V₂, . . . , V_(P) respectively denote direction coherences V_(θ)(l,m) (where θ=θ₁, θ₂, . . . , θ_(P)) with respect to P directions θ₁, θ₂, . . . , θ_(P) (where P is 2 or an integer greater than 2), respectively, in the partial image M_(N×N)(l,m); max{V₁, V₂, . . . , V_(P)} denotes a maximum value of the direction coherences V_(θ)(l,m); and min{V₁, V₂, . . . , V_(P)} denotes a minimum value of the direction coherences V_(θ)(l,m).

Besides the test statistic defined by Equation (14), a test statistic defined by Equation (15) or (16) may also be used.

$\begin{matrix} {Z = \frac{X_{P - 1}}{X_{2}}} & (15) \\ {Z = \frac{X_{P} + X_{P - 1}}{X_{1} + X_{2}}} & (16) \end{matrix}$

More typically, a test statistic defined by Equation (17) may be used.

$\begin{matrix} {Z = \frac{X_{j}}{X_{i}}} & (17) \end{matrix}$

In Equations (15) through (17), X₁ denotes the first minimum value of the direction coherences V_(θ)(l,m) with respect to the P directions θ₁, θ₂, . . . , θ_(P) in the partial image M_(N×N)(l,m), X₂ denotes. the second minimum value of them, and X_(i) denotes the i-th minimum value of them. In this manner, X_(p) denotes the first maximum value of the direction coherences V_(θ)(l,m) and X_(p−1) denotes the second maximum value of them.

In operation S400, the statistical hypothesis test unit 40 compares the test statistic Z obtained in operation S300 with the one or more predetermined image/noise limits β₀ and β₁ to obtain the image/noise detection value H(l,m) indicating the amount of image/noise feature in the partial image M_(N×N)(l,m).

In the current embodiment of the present invention, the two image/noise limits β₀ and β₁ are used. However, a single image/noise limit or at least three image/noise limits may be used in other embodiments of the present invention.

Operation S400 in the statistical image processing method illustrated in FIG. 4 will be described in detail with reference to FIG. 8 below.

Referring to FIG. 8, at least one image/noise limits β₀ and β₁ for determination of image/noise detection value H are set in operation S410. When the test statistic Z obtained in operation S300 is at least a first image/noise limit (hereinafter, referred to as a pure image limit β₁), the image/noise detection value H(l,m) is set to a first setting value (e.g., “1”) in operation S420. Here, the pure image limit β₁denotes that the image/noise detection value H(l,m) is no longer the first setting value when the test statistic Z is less than a value of β₁.

Contrarily, when the test statistic Z does not exceed a pure noise limit β₀, the image/noise detection value H(l,m) is set to a second setting value (e.g., “0”) in operation S430. Here, the pure noise limit β₀ denotes that the image/noise detection value H(l,m) is no longer the second setting value when the test statistic Z is greater than a value of β₀.

When the test statistic Z is in a range of the pure noise limit β₀ to the pure image limit β₁, the image/noise detection value H(l,m) is set to an increasing function of the test statistic Z having a value from the first to the second setting value, e.g., 0 to 1, in operation S440.

In other words, the procedure for obtaining the image/noise detection value H(l,m) using the predetermined pure noise and image limits β₀ and β₁ in operation S400, i.e., S410 through S440, may end in one of the following three cases.

In the first case, if Z>β₁, the null hypothesis H₀ is rejected and “unequal variance” is determined. That is, it is determined that an image feature exists.

In the second case, if Z<β₀, the null hypothesis H₀ is selected and “equal variance” is determined. That is, it is determined that an image feature does not exist.

In the third case, if β₀≦Z≦β₁, determination is deferred. Here, intermediary determination is made about the existence/non-existence of an image feature, which will be described with reference to FIGS. 9 through 11.

FIG. 9 is a graph of an image/noise detection function determined in operation S440 according to some embodiments of the present invention. Here, the image/noise detection value H(l,m) is expressed by Equation (18).

$\begin{matrix} {{H\left( {l,m} \right)} = \left\{ \begin{matrix} {0,} & {{{if}\mspace{14mu} z} < \beta_{0}} \\ {\frac{z - \beta_{0}}{\beta_{1} - \beta_{0}},} & {{{if}\mspace{14mu}\beta_{0}} \leq z \leq \beta_{1}} \\ {1,} & {{{if}\mspace{14mu} z} > \beta_{1}} \end{matrix} \right.} & (18) \end{matrix}$

Here, H(l,m) denotes an image/noise detection value for a pixel positioned on the l-th row and the m-th column in an entire image, Z denotes a test statistic, β₀ denotes the pure noise limit, and β₁ denotes the pure image limit. When the image/noise detection value is expressed by Equation (18), it changes linearly.

FIG. 10 is a graph of an image/noise detection function determined in operation S440 according to some embodiments of the present invention. Here, the image/noise detection value H(l,m) is expressed by Equation (19).

$\begin{matrix} {{H\left( {l,m} \right)} = \left\{ \begin{matrix} {0,} & {{{if}\mspace{14mu} z} < \beta_{0}} \\ {\left( \frac{z - \beta_{0}}{\beta_{1} - \beta_{0}} \right)^{2},} & {{{if}\mspace{14mu}\beta_{0}} \leq z \leq \beta_{1}} \\ {1,} & {{{if}\mspace{14mu} z} > \beta_{1}} \end{matrix} \right.} & (19) \end{matrix}$

Here, H(l,m) denotes an image/noise detection value for a pixel positioned on the l-th row and the m-th column in an entire image, Z denotes a test statistic, β₀ denotes the pure noise limit, and β₁ denotes the pure image limit. When the image/noise detection value is expressed by Equation (19), it changes slowly first and then changes rapidly.

FIG. 11 is a graph of an image/noise detection function determined in operation S440 according to some embodiments of the present invention. Here, the image/noise detection value H(l,m) is expressed by Equation (20).

$\begin{matrix} {{H\left( {l,m} \right)} = \left\{ \begin{matrix} {0,} & {{{if}\mspace{14mu} z} < \beta_{0}} \\ {{1 - \left( \frac{z - \beta_{1}}{\beta_{1} - \beta_{0}} \right)^{2}},} & {{{if}\mspace{14mu}\beta_{0}} \leq z \leq \beta_{1}} \\ {1,} & {{{if}\mspace{14mu} z} > \beta_{1}} \end{matrix} \right.} & (20) \end{matrix}$

Here, H(l,m) denotes an image/noise detection value for a pixel positioned on the I-th row and the m-th column in an entire image, Z denotes a test statistic, β₀ denotes the pure noise limit, and β₁ denotes the pure image limit. When the image/noise detection value is expressed by Equation (20), it changes rapidly first and then changes slowly.

FIG. 12 is a detailed flowchart of a procedure for obtaining the two image/noise limits β₀ and β₁ using the direction coherence V_(θ)(l,m) in the method illustrated in FIG. 4. In other words, FIG. 12 illustrates a procedure for calculating the pure noise limit β₀ and the pure image limit β₁, which have been described as values predetermined to obtain the image/noise detection value H(l,m) in operation S400. In operation S410, the pure noise limit β₀ and the pure image limit β₁ are calculated using significant levels and the direction coherences V_(θ)(l,m) obtained in operation S200.

Referring to FIG. 12, on the assumption that the plurality of direction coherences V_(θ) (where θ=θ₁, θ₂, . . . , θ_(P)) are, independent of each other and comply with chi-square distribution having a certain degree of freedom, a probability density function h(z) is obtained with respect to the test statistic Z in operation S411. In a case where θ=1, 2, 3, 4, that is, when direction coherences V₁, V₂, V₃, and V₄ are given, if V₁, V₂, V₃, and V₄ are independent of one another and comply with chi-square distribution having the degree of freedom “r” and the test statistic Z is given by Equation (21), the probability density function h(z) of the test statistic Z is defined by Equation (22).

$\begin{matrix} {Z = \frac{X_{4}}{X_{1}}} & (21) \end{matrix}$

Here, X₁ denotes a minimum value among V₁, V₂, V₃, and V₄ and X₄ denotes a maximum value among V₁, V₂, V₃, and V₄.

$\begin{matrix} {{{h(z)} = {\frac{12\mspace{11mu} z^{\frac{r}{2} - 1}}{{\Gamma\left( \frac{r}{2} \right)}{\Gamma\left( \frac{r}{2} \right)}2^{r}} \times {\int_{0}^{\infty}{w^{r - 1}{{\mathbb{e}}^{- \frac{w{({1 + z})}}{2}}\left\lbrack {\int_{w}^{zw}{\frac{t^{\frac{r}{2} - 1}{\mathbb{e}}^{- \frac{r}{2}}}{{\Gamma\left( \frac{r}{2} \right)}2^{r}}\ {\mathbb{d}t}}} \right\rbrack}^{2}\ {\mathbb{d}w}}}}},{z \geq 1}} & (22) \end{matrix}$

In operation S412, a value of z, at which a distribution of the probability density function h(z) obtained when the test statistic Z is less than a predetermined value occupies less than a ratio of α₀ in an entire range defined by the probability density function h(z), is calculated and set as the pure noise limit β₀. Referring to a graph illustrated in operation S412, a value of z, at which a hatched area occupies α₀ in the entire area between the Z axis and the probability density function h(z), is set as the pure noise limit β₀. Here, the value of α₀ may be a predetermined stationary value or may be changed by a user.

In operation S413, a value of z, at which a distribution of the probability density function h(z) obtained when the test statistic Z is greater than the predetermined value occupies less than a ratio of α₁ in the entire range defined by the probability density function h(z), is calculated and set as the pure image limit β₁. Referring to a graph illustrated in operation S413, a value of z, at which a hatched area occupies α₁ in the entire area between the Z axis and the probability density function h(z), is set as the pure image limit β₁. Here, the value of α₁ may be a predetermined stationary value or may be changed by a user.

The image/noise detection value H(l,m) is obtained using the thus calculated pure noise limit β₀ and the pure image limits β₁. The image/noise detection value H(l,m) obtained by the above-described procedure may be used for applications in various fields of image processing. The above-described method may be generalized as illustrated in FIG. 13 to be used for image processing.

FIG. 13 is a block diagram of a real application using a statistical image processing system and method for detecting an image/noise feature according to some embodiments of the present invention. In FIG. 13, f(l,m) and p(l,m) respectively indicate an input image signal and an output signal of an image processing block 50 and y(l,m) indicates a final output signal adjusted according to an image detection signal. The image processing block 50 performs normal image processing used in image enhancement. An image sensing block 60 outputs the amount of image feature in the input image signal using the above-described hypothesis test. The image sensing block 60 may correspond to the statistical image processing system illustrated in FIG. 3. An output signal of the image sensing block 60, i.e., an image detection signal H(l,m) may be used for sharpness enhancement, smoothing, noise reduction, deinterlacing, contrast enhancement, etc. An image mixing block 70 mixes the input image signal f(l,m) and the processed image p(l,m) according to the image detection signal H(l,m) output from the image sensing block 60. In other words, the original image f(l,m) and the result image p(l,m) of image processing are appropriately mixed based on. the hypothesis test result H(l,m) output from the image sensing block 60 according to the purpose of entire image processing to obtain the final result image y(l,m). The linear or non-linear method defined by Equation (18), (19), or (20) may be used as the mixing method according to an entire purpose.

According to some embodiments of the present invention, existence/non-existence of an image/noise feature in an input image can be accurately detected regardless of the magnitude of a sample variance of pixel data in the input image. In addition, the relative amount of noise and/or image feature can be numerically expressed and can be used in the various fields of image processing application.

While the present invention has been shown and described with reference to exemplary embodiments thereof, it will be understood by those of ordinary skill in the art that various changes in form and detail may be made herein without departing from the spirit and scope of the present invention, as defined by the following claims. 

1. An image sensing block of a statistical image processing system for detecting an image/noise feature, the image sensing block comprising: a partial image extraction unit configured to extract a partial image surrounding a particular pixel in an input image; an image correlation estimation unit, recorded on a non-transitory computer-readable medium, configured to obtain a direction coherence with respect to each of a plurality of predetermined direction using a correlation between pixel values in the partial image; an independence estimation unit configured to obtain a test statistic by numerically expressing similarity between a plurality of direction coherences obtained by the image correlation estimation unit; and a statistical hypothesis test unit configured to compare the test statistic obtained by the independence estimation unit with at least one predetermined image/noise limit and to obtain an image/noise detection value indicating an amount of image/noise feature in the partial image.
 2. The system of claim 1, wherein each of the plurality of direction coherences is set based on differences between adjacent pixel values which are arranged in each of the plurality of directions in the partial image.
 3. The system of claim 1, wherein each of the plurality of direction coherences is set based on differences between pixels in the partial image and respective pixels apart from the pixels, respectively, in the partial image by a predetermined distance defined by a row and a column.
 4. The system of claim 1, wherein the image/noise detection value is set to a first setting value when the test statistic is at least a first image/noise limit, is set to a second setting value when the test statistic does not exceed a second image/noise limit, and is set to a function of the test statistic, which has a value between the first setting value and the second setting value, when the test statistic is between the first image/noise limit and the second image/noise limit.
 5. A statistical image processing method for detecting an image/noise feature, the method comprising: extracting a partial image surrounding a particular pixel in an input image; obtaining, in an image sensing block of a statistical image processing system, a direction coherence with respect to each of a plurality of predetermined direction using a correlation between pixel values in the partial image; obtaining, in the image sensing block of the statistical image processing system, a test statistic by numerically expressing similarity between a plurality of direction coherences; and obtaining, in the image sensing block of the statistical image processing system, an image/noise detection value indicating an amount of image/noise feature in the partial image by comparing the test statistic with at least one predetermined image/noise limit wherein the statistical image processing system includes a processor.
 6. The method of claim 5, wherein obtaining the direction coherence comprises: arranging pixels values in the partial image on a line along a predetermined direction; and defining the direction coherence based on a function having as a factor a difference Z_(i) ^(θ) between adjacent pixel values, which is expressed by z_(i) ^(θ)=u_(i) ^(θ)−u¹⁻¹ ^(θ) or z_(i) ^(θ)=|u_(i) ^(θ)−u_(i−1) ^(θ)| where “θ” indicates an angle of a direction in which all pixel values in the partial image are read and “u_(i) ^(θ)” indicates an i-th pixel value when the pixel values in the partial image are sequentially read at the direction angle θ.
 7. The method of claim 6, wherein, when all pixels in the partial image are arranged in an N×N matrix, the direction coherence is defined by one of the following equations: ${V_{\theta} = {\frac{1}{N^{2} - 1}{\sum\limits_{i = 2}^{N^{2}}\left( z_{i}^{\theta} \right)^{2}}}},{V_{\theta} = {\frac{1}{N^{2} - 1}{\sum\limits_{i = 2}^{N^{2}}{z_{i}^{\theta}}}}},{V_{\theta} = {\frac{1}{N^{2} - 1}{\sum\limits_{i = 2}^{N^{2}}\left( {z_{i}^{\theta} - \overset{\_}{z}} \right)^{2}}}},{\overset{\_}{z} = {\frac{1}{N^{2} - 1}{\sum\limits_{i = 2}^{N^{2}}z_{i}^{\theta}}}},{V_{\theta} = {\frac{1}{N^{2} - 1}{\sum\limits_{i = 2}^{N^{2}}\left( {\left\lbrack z_{i}^{\theta} \right\rbrack^{2} - \overset{\_}{z}} \right)^{2}}}},{\overset{\_}{z} = {\frac{1}{N^{2} - 1}{\sum\limits_{i = 2}^{N^{2}}z_{i}^{\theta}}}},{and}$ ${V_{\theta} = {\frac{1}{N^{2} - 1}{\sum\limits_{i = 2}^{N^{2}}\left( {{z_{i}^{\theta}} - \overset{\_}{z}} \right)^{2}}}},{\overset{\_}{z} = {\frac{1}{N^{2} - 1}{\sum\limits_{i = 2}^{N^{2}}{z_{i}^{\theta}.}}}}$
 8. The method of claim 5, wherein obtaining the direction coherence comprises: defining a second partial image comprising pixels respectively apart from all pixels in the partial image, which corresponds to a first partial image, by a predetermined distance defined by a row and a column; and defining the direction coherence based on a function having as a factor a difference z_(i,j) ^(θ) between a pixel value in the first partial image and a corresponding pixel in the second partial image, which is expressed by one of the following equations: ${z_{i,j}^{\theta} = {{x_{i,j}\left( {l,m} \right)} - {x_{i,j}\left( {{l + \Delta_{l}},{m + \Delta_{m}}} \right)}}},{\theta = {\tan^{- 1}\left( \frac{\Delta_{l}}{\Delta_{m}} \right)}},{and}$ ${z_{i,j}^{\theta} = {{{x_{i,j}\left( {l,m} \right)} - {x_{i,j}\left( {{l + \Delta_{l}},{m + \Delta_{m}}} \right)}}}},{\theta = {\tan^{- 1}\left( \frac{\Delta_{l}}{\Delta_{m}} \right)}},$ where x_(i,j)(l,m) indicates a pixel value on an i-th row and a j-th column in the first partial image and x_(i,j)(l+Δ_(l),m+Δ_(m)) indicates a pixel value on an i-th row and a j-th column in the second partial image apart from the first partial image by Δ_(l) rows and Δ_(m) columns.
 9. The method of claim 8, wherein, when all pixels in the partial image are arranged in an N×N matrix, the direction coherence is defined by one of the following equations: ${{V_{\theta}\left( {l,m} \right)} = {\frac{1}{N^{2}}{\sum\limits_{i = 1}^{N}{\sum\limits_{j = 1}^{N}\left( z_{i,j}^{\theta} \right)^{2}}}}},{{V_{\theta}\left( {l,m} \right)} = {\frac{1}{N^{2}}{\sum\limits_{i = 1}^{N}{\sum\limits_{j = 1}^{N}{z_{i,j}^{\theta}}}}}},{{V_{\theta}\left( {l,m} \right)} = {\frac{1}{N^{2}}{\sum\limits_{i = 1}^{N}{\sum\limits_{j = 1}^{N}\left( {z_{i,j}^{\theta} - \overset{\_}{z}} \right)^{2}}}}},{\overset{\_}{z} = {\frac{1}{N^{2}}{\sum\limits_{i = 1}^{N}{\sum\limits_{j = 1}^{N}z_{i,j}^{\theta}}}}},{{V_{\theta}\left( {l,m} \right)} = {\frac{1}{N^{2}}{\sum\limits_{i = 1}^{N}{\sum\limits_{j = 1}^{N}\left( {\left\lbrack z_{i,j}^{\theta} \right\rbrack^{2} - \overset{\_}{z}} \right)^{2}}}}},{\overset{\_}{z} = {\frac{1}{N^{2}}{\sum\limits_{i = 1}^{N}{\sum\limits_{j = 1}^{N}z_{i,j}^{\theta}}}}},{and}$ ${{V_{\theta}\left( {l,m} \right)} = {\frac{1}{N^{2}}{\sum\limits_{i = 1}^{N}{\sum\limits_{j = 1}^{N}\left( {{z_{i,j}^{\theta}} - \overset{\_}{z}} \right)^{2}}}}},{\overset{\_}{z} = {\frac{1}{N^{2}}{\sum\limits_{i = 1}^{N}{\sum\limits_{j = 1}^{N}{z_{i,j}^{\theta}.}}}}}$
 10. The method of claim 5, wherein obtaining the image/noise detection value comprises: setting the image/noise detection value to a first setting value when the test statistic is at least a first image/noise limit; setting the image/noise detection value to a second setting value when the test statistic does not exceed a second image/noise limit; and setting the image/noise detection value to a function of the test statistic, which has a value between the first setting value and the second setting value, when the test statistic is between the first image/noise limit and the second image/noise limit.
 11. The method of claim 10, wherein the image/noise detection value is given by: ${H\left( {l,m} \right)} = \left\{ \begin{matrix} {0,} & {{{if}\mspace{14mu} z} < \beta_{0}} \\ {\frac{z - \beta_{0}}{\beta_{1} - \beta_{0}},} & {{{if}\mspace{14mu}\beta_{0}} \leq z \leq \beta_{1}} \\ {1,} & {{{{if}\mspace{14mu} z} > \beta_{1}},} \end{matrix} \right.$ where H(l,m) denotes the image/noise detection value for a pixel positioned on an l-th row and an m-th column in an entire image, Z denotes the test statistic, β₁ denotes the first image/noise limit, and β₀ denotes the second image/noise limit.
 12. The method of claim 10, wherein the image/noise detection value is given by: ${H\left( {l,m} \right)} = \left\{ \begin{matrix} {0,} & {{{if}\mspace{14mu} z} < \beta_{0}} \\ {\left( \frac{z - \beta_{0}}{\beta_{1} - \beta_{0}} \right)^{2},} & {{{if}\mspace{14mu}\beta_{0}} \leq z \leq \beta_{1}} \\ {1,} & {{{{if}\mspace{14mu} z} > \beta_{1}},} \end{matrix} \right.$ where H(l,m) denotes the image/noise detection value for a pixel positioned on an l-th row and an m-th column in an entire image, Z denotes the test statistic, β₁ denotes the first image/noise limit, and β₀ denotes the second image/noise limit.
 13. The method of claim 10, wherein the image/noise detection value is given by: ${H\left( {l,m} \right)} = \left\{ \begin{matrix} {0,} & {{{if}\mspace{14mu} z} < \beta_{0}} \\ {{1 - \left( \frac{z - \beta_{1}}{\beta_{1} - \beta_{0}} \right)^{2}},} & {{{if}\mspace{14mu}\beta_{0}} \leq z \leq \beta_{1}} \\ {1,} & {{{{if}\mspace{14mu} z} > \beta_{1}},} \end{matrix} \right.$ where H(l,m) denotes the image/noise detection value for a pixel positioned on an l-th row and an m-th column in an entire image, Z denotes the test statistic, β₁ denotes the first image/noise limit, and β₀ denotes the second image/noise limit.
 14. The method of claim 10, wherein the first image/noise limit β₁ has a value of z, at which a distribution of a probability density function h(z) obtained when the test statistic is greater than a predetermined value occupies less than a ratio of a_(l) in an entire range defined by the probability density function h(z), where the probability density function h(z) is obtained with respect to the test statistic when the plurality of direction coherences are independent of each other and comply with chi-square distribution having a certain degree of freedom.
 15. The method of claim 10, wherein the second image/noise limit β₀ has a value of z, at which a distribution of a probability density function h(z) obtained when the test statistic is less than a predetermined value occupies less than a ratio of α₀ in an entire range defined by the probability density function h(z), where the probability density function h(z) is obtained with respect to the test statistic when the plurality of direction coherences are independent of each other and comply with chi-square distribution having a certain degree of freedom.
 16. The method of claim 5, wherein the test statistic is given by: ${Z = \frac{\max\begin{Bmatrix} {V_{1},} & {V_{2},} & {\cdots\mspace{11mu},} & V_{P} \end{Bmatrix}}{\min\begin{Bmatrix} {V_{1},} & {V_{2},} & {\cdots\mspace{11mu},} & V_{P} \end{Bmatrix}}},$ where V₁, V₂, . . . , V_(P) respectively denote direction coherences with respect to P directions, respectively, in the partial image, max{V₁, V₂, . . . , V_(P)} denotes a maximum value of the direction coherences, and min{V₁, V₂, . . . , V_(P)} denotes a minimum value of the direction coherences.
 17. The method of claim 5, wherein the test statistic is given by: ${Z = \frac{X_{j}}{X_{i}}},$ where X_(i) denotes an i-th minimum value of the direction coherences with respect to the P directions in the partial image and x_(j) denotes a j-th minimum value of the direction coherences.
 18. The method of claim 5, wherein the test statistic is given by: ${Z = \frac{X_{P} + X_{P - 1}}{X_{1} + X_{2}}},$ where X₁ denotes a first minimum value of the direction coherences with respect to P directions in the partial image, X₂ denotes a second minimum value of the direction coherences, X_(p) denotes a first maximum value of the direction coherences, and X_(p−1) denotes a second maximum value of the direction coherences.
 19. A non-transitory computer-readable medium including program segments for causing, when executed on a image sensing block of a statistical image processing system, the image sensing block to implement the method of claim
 5. 20. An image sensing block of a statistical image processing system for detecting an image/noise feature, the image sensing block comprising: a partial image extraction unit configured to extract a partial image surrounding a particular pixel in an input image; an image correlation estimation unit configured to obtain a direction coherence with respect to each of a plurality of predetermined direction using a correlation between pixel values in the partial image; an independence estimation unit, recorded on a non-transitory computer-readable medium, configured to obtain a test statistic by numerically expressing similarity between a plurality of direction coherences obtained by the image correlation estimation unit; and a statistical hypothesis test unit configured to compare the test statistic obtained by the independence estimation unit with at least one predetermined image/noise limit and to obtain an image/noise detection value indicating an amount of image/noise feature in the partial image. 